Our approach provides two different adaptation methods, adapting based on discrete or continuous labels. The teacher network pseudo-labels are initially filtered to remove parts representing the human moving using the output human segmentation from Mask R-CNN [1]}. When discrete labels are used it is based on pseudo-labels from the confident pixels in the teacher network output. Such a method provides superior accuracy, but on the expense of tuning the parameters that determine these confident pixels. Another method that utilizes continuous labels adaptation from the teacher network is also introduced. This method alleviates the need for any hyper-parameter tuning but on the cost of degraded accuracy. Figure REF  summarizes the adaptation scheme, and shows the output pseudo-labels, the output segmentation before and after adaptation.
